A Spatial Network Model for Civil Infrastructure System Development. (1st June 2016)

• Record Type: Journal Article
• Authors: Fu, Gaihua; Wilkinson, Sean; Dawson, Richard J.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 31:Number 9(2016:Sep.)
• Page Start: 661
• Page End: 680
• Publication Date: 2016-06-01
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12204
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Infrastructure networks play an important role in improving economic prosperity, enabling movement of resources, and protecting communities from hazards. As these networks serve population, they evolve in response to social, economic, environmental, and technological changes. Consideration of these interactions has thus far been limited by use of simplified data sets and idealized network structures, and is unable to explain the complexity and suboptimal structures displayed by real infrastructure networks. This article presents a new computational model that simulates the growth and evolution of infrastructure systems. Empirical evidence obtained from analysis of nontrivial real‐world data sets is used to identify the mechanisms that guide and govern system‐scale evolution of infrastructure networks. The model investigates the interplay of three key drivers, namely network demand, network efficiency, and network cost in shaping infrastructure network architectures. The validity of the model is verified by comparing key topological and spatial properties of simulated networks with real‐world networks from six infrastructure sectors. The model is used to develop and explore different scenarios of infrastructure network futures, and their resilience is shown to change as a result of different infrastructure management policies. The model can therefore be used to identify system‐wide infrastructure engineering strategies to reduce network costs, increase network efficiency, and improve the resilience of infrastructure networks to disruptive events.